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A state space myoelectricity model construction method based on forward dynamics and a Hille model

A forward dynamics, state space technology, applied in the field of EMG signal processing, can solve problems such as cumulative error, practical application limitation, and lower prediction accuracy, so as to reduce cumulative error, improve accuracy, reduce system error and external the effect of interference

Active Publication Date: 2019-06-14
HANGZHOU DIANZI UNIV
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Problems solved by technology

The model involves multiple physiological parameters, which is difficult to calculate and limited in practical application
HMM is the most commonly used muscle model for estimating continuous joint motion, but there are two problems: one is that HMM involves many complex physiological parameters that are difficult to identify, and the calculation burden is relatively large; the other is that HMM can directly calculate joint torque from sEMG signals, But if you need continuous joint motion estimation, you also need to calculate the motion state from the torque
This often introduces cumulative errors, reducing forecast accuracy

Method used

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  • A state space myoelectricity model construction method based on forward dynamics and a Hille model
  • A state space myoelectricity model construction method based on forward dynamics and a Hille model
  • A state space myoelectricity model construction method based on forward dynamics and a Hille model

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Embodiment Construction

[0039] Such as figure 1 As shown, this embodiment includes the following steps:

[0040] Step 1: Establish a simplified Hill muscle model.

[0041] 1) First, the muscle activation is obtained from the nerve activation:

[0042]

[0043] a(k) is muscle activation, u(k) is nerve activation, k is time, and A is a nonlinear shape factor that defines the curvature of the function.

[0044] 2) Perform parameter substitution on the Hill muscle model:

[0045]

[0046] f mt is the tendon force, f A (l) is the normalized active force-length relation, f V (v) is the normalized force-velocity relationship, f P (l) is the normalized passive force-length relation, F 0 m is the maximum isometric force, and φ is the plume angle.

[0047] 3) The joint torque expression is as follows:

[0048] τ=F mt r

[0049] τ is the torque and r is the moment arm.

[0050] Step two, combine forward dynamics with the Hill muscle model.

[0051] 1) The angular acceleration of the knee joi...

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Abstract

The invention relates to a state space myoelectricity model construction method based on forward dynamics and a Hille model. The method comprises the following steps of acquiring the electromyographicsignals of related muscles of a joint in a continuous motion state; carrying out band-pass filtering processing on the signals; obtaining the related muscle activation through nerve activation; substituting the model into a Hille muscle model; carrying out simplification and parameter substitution on the Hille muscle model; combining the replaced simplified model with joint forward dynamics; obtaining a prediction model in a discrete time state according to the acquired related electromyographic signals, carrying out root-mean-square and wavelet coefficient feature extraction on the acquiredrelated electromyographic signals, forming a measurement equation as state feedback, and carrying out fitting with joint motion through a fitting equation to obtain a final state space electromyographic model. Compared with a traditional angle estimation method, the model is obviously improved at the aspects of prediction precision, real-time performance and the like.

Description

technical field [0001] The invention belongs to the field of myoelectric signal processing, and relates to the construction of a state space myoelectric model, in particular to a state space myoelectric model based on forward dynamics and a Hill muscle model. Background technique [0002] Surface Electromyography (sEMG) is a popular input signal source for human-computer interaction in cutting-edge science and technology. Action potential sequence, which has obvious characteristics, rich information, and simple and non-invasive acquisition, has become a hot research field in human-computer interaction technology. The research on surface EMG mainly focuses on the two processes of feature extraction and pattern recognition. The corresponding research results are relatively mature and can identify multiple discrete action categories. However, in the fields of rehabilitation medical robots and other fields, it is more often necessary to predict the continuous motion variables ...

Claims

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Application Information

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IPC IPC(8): G06F3/01G06F17/50
Inventor 席旭刚杨晨范影乐石鹏袁长敏章燕张启忠罗志增
Owner HANGZHOU DIANZI UNIV
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